All text that follows is from Rich Hickey via https://www.linkedin.com/pulse/ai-tool-rich-hickey-kjl8e

by Rich Hickey Published Oct 12, 2026

(I keep delivering this as a quip, here's a bit more)

First order of business - is 'tool' going to have an interesting, distinguishing and thus illuminative meaning or be a mere platitudinous stand-in for "anything useful"?

RAGE code provides and supports basic functionality that all projects at Rockstar San Diego can build on. However, no one game takes advantage of all of RAGE’s capabilities. RAGE is intended to provide a cross-platform abstraction layer for many common pieces of functionality that most games will require. Each specific game should select only the RAGE functionality that they need. In turn, RAGE should not contain game-specific code.

RAGE is not intended to be a complete "turn-key" game development engine, though as more general-purpose elements are migrated from games down into RAGE, it could move in this direction.

The qualification for code being part of RAGE is that it provide functionality which most projects need, at least as a starting point. It is strongly recommended that projects link directly with the RAGE modules, rather than taking copies of the RAGE code and putting their own version into their project-specific code hierarchy. RAGE shares nuts-and-bolts functionality, production to

Hugo is a static site generator: it takes some plain-text content, marries it to a bunch of HTML templates, and produces a set of complete, static HTML pages that can be served by any generic, stand-alone web server. Simple.

Or maybe not. Hugo does a lot of things automatically, relying on conventions and implicit rules, rather than on explicit configuration. For example, it tries to match each piece of content with the most

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

H3 uses a hierarchical system where the world is divided into hexagonal cells at different resolutions, allowing for multi-resolution indexing.

• Resolution 0: The entire world is divided into 122 base hexagons.
• Higher resolutions (1-15): Each base hexagon is subdivided into smaller hexagons.
• Resolution 15: The smallest possible hexagons.